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{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"id": "83d8d249-affe-45dd-915e-992b4b35b31a",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import numpy as np\n",
"import pandas as pd\n",
"import deepsort\n",
"from sklearn.metrics import accuracy_score, f1_score\n",
"from tqdm.notebook import tqdm\n",
"import pickle"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "25de46ec-8a41-484d-8e14-d2b19768fc2c",
"metadata": {},
"outputs": [],
"source": [
"def compute_metrics(labels, preds):\n",
"\n",
" # calculate accuracy and macro f1 using sklearn's function\n",
" acc = accuracy_score(labels, preds)\n",
" macro_f1 = f1_score(labels, preds, average='macro')\n",
" return {\n",
" 'accuracy': acc,\n",
" 'macro_f1': macro_f1\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "a4029b2b-afca-4300-82a2-082fec59f191",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['pancreas',\n",
" 'liver',\n",
" 'blood',\n",
" 'lung',\n",
" 'spleen',\n",
" 'placenta',\n",
" 'colorectum',\n",
" 'kidney',\n",
" 'brain']"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rootdir = \"/path/to/data/\"\n",
"\n",
"dir_list = []\n",
"for dir_i in os.listdir(rootdir):\n",
" if (\"results\" not in dir_i) & (os.path.isdir(os.path.join(rootdir, dir_i))):\n",
" dir_list += [dir_i]\n",
"dir_list"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ddcdc5cd-871e-4fd2-8457-18d3049fa76c",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"output_dir = \"results_EDefault_filtered\"\n",
"n_epochs = \"Default\" # scDeepsort default epochs = 300\n",
"\n",
"results_dict = dict()\n",
"for dir_name in tqdm(dir_list):\n",
" print(f\"TRAINING: {dir_name}\")\n",
" subrootdir = f\"{rootdir}{dir_name}/\"\n",
" train_files = [(f\"{subrootdir}{dir_name}_filtered_data_train.csv\",f\"{subrootdir}{dir_name}_filtered_celltype_train.csv\")]\n",
" test_file = f\"{subrootdir}{dir_name}_filtered_data_test.csv\"\n",
" label_file = f\"{subrootdir}{dir_name}_filtered_celltype_test.csv\"\n",
" \n",
" # define the model\n",
" model = deepsort.DeepSortClassifier(species='human',\n",
" tissue=dir_name,\n",
" gpu_id=0,\n",
" random_seed=1,\n",
" validation_fraction=0) # use all training data (already held out 20% in test data file)\n",
"\n",
" # fit the model\n",
" model.fit(train_files, save_path=f\"{subrootdir}{output_dir}\")\n",
" \n",
" # use the saved model to predict cell types in test data\n",
" model.predict(input_file=test_file,\n",
" model_path=f\"{subrootdir}{output_dir}\",\n",
" save_path=f\"{subrootdir}{output_dir}\",\n",
" unsure_rate=0,\n",
" file_type='csv')\n",
" labels_df = pd.read_csv(label_file)\n",
" preds_df = pd.read_csv(f\"{subrootdir}{output_dir}/human_{dir_name}_{dir_name}_filtered_data_test.csv\")\n",
" label_cell_ids = labels_df[\"Cell\"]\n",
" pred_cell_ids = preds_df[\"index\"]\n",
" assert list(label_cell_ids) == list(pred_cell_ids)\n",
" labels = list(labels_df[\"Cell_type\"])\n",
" if isinstance(preds_df[\"cell_subtype\"][0],float):\n",
" if np.isnan(preds_df[\"cell_subtype\"][0]):\n",
" preds = list(preds_df[\"cell_type\"])\n",
" results = compute_metrics(labels, preds)\n",
" else:\n",
" preds1 = list(preds_df[\"cell_type\"])\n",
" preds2 = list(preds_df[\"cell_subtype\"])\n",
" results1 = compute_metrics(labels, preds1)\n",
" results2 = compute_metrics(labels, preds2)\n",
" if results2[\"accuracy\"] > results1[\"accuracy\"]:\n",
" results = results2\n",
" else:\n",
" results = results1\n",
" \n",
" print(f\"{dir_name}: {results}\")\n",
" results_dict[dir_name] = results\n",
" with open(f\"{subrootdir}deepsort_E{n_epochs}_filtered_pred_{dir_name}.pickle\", \"wb\") as output_file:\n",
" pickle.dump(results, output_file)\n",
"\n",
"# save results\n",
"with open(f\"{rootdir}deepsort_E{n_epochs}_filtered_pred_dict.pickle\", \"wb\") as output_file:\n",
" pickle.dump(results_dict, output_file)\n",
" "
]
}
],
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"kernelspec": {
"display_name": "Python 3.8.6 64-bit ('3.8.6')",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.6"
},
"vscode": {
"interpreter": {
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}
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"nbformat": 4,
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